Psychiatry

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.

Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In this study, we first investigated statistical differences in large-scale resting-state networks (RSNs) on 192 healthy controls (HCs) and 103 ASD patients...

Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.

We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who underwent cardiac surgery within five years of age. At least 18 months after cardiac surgery, we performed an extensive neuropsychological (intelligenc...

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks ...

Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables.

OBJECTIVES: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.

Mediating artificial intelligence developments through negative and positive incentives.

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, busines...

Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electro...

Conversational Agents for Chronic Disease Self-Management: A Systematic Review.

We conducted a systematic literature review to assess how conversational agents have been used to facilitate chronic disease self-management. The Pref...

Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening me...

User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health rec...

Analyzing Description, User Understanding and Expectations of AI in Mobile Health Applications.

Previous research has studied medical professionals' perception of artificial intelligence (AI). However, there has been a limited understanding of ho...

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities.

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM ca...

Approaches to assessing the impact of robotics in geriatric mental health care: a scoping review.

The goals of this scoping literature review are to (1) aggregate the current research involving socially assistive robots in the setting of geriatric ...

Identifying intentional injuries among children and adolescents based on Machine Learning.

BACKGROUND: Compared to other studies, the injury monitoring of Chinese children and adolescents has captured a low level of intentional injuries on a...

Let's not be indifferent about robots: Neutral ratings on bipolar measures mask ambivalence in attitudes towards robots.

Ambivalence, the simultaneous experience of both positive and negative feelings about one and the same attitude object, has been investigated within p...

A natural language processing approach for identifying temporal disease onset information from mental healthcare text.

Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential....

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.

Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the si...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors deter...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neur...

Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes.

Machine learning has been suggested as a means of identifying individuals at greatest risk for hospital readmission, including psychiatric readmission...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...